Odds ratio function estimation using a generalized additive neural network

Brás-Geraldes, Carlos; Papoila, Ana Luísa; Xufre, Patrícia

doi:10.1007/s00521-019-04189-7

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…Our method relates also to Generalised Additive Neural Networks (GANNs), sometimes also called Self-Explanatory Neural Networks (SENN), which have a long history [8][9][10][11]. Models [8][9][10] have the structure of a GAM when applied to shallow neural networks, similar to our models. However, none of these papers deals with the derivation of the model structure.…”

Section: Related Workmentioning

confidence: 99%

Towards interpretable machine learning for clinical decision support

Walters

Ortega‐Martorell

Olier

et al. 2022

2022 International Joint Conference on Neural Networks (IJCNN)

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A major challenge in delivering reliable and trustworthy computational intelligence for practical applications in clinical medicine is interpretability. This aspect of machine learning is a major distinguishing factor compared with traditional statistical models for the stratification of patients, which typically use rules or a risk score identified by logistic regression.We show how functions of one and two variables can be extracted from pre-trained machine learning models using anchored Analysis of Variance (ANOVA) decompositions. This enables complex interaction terms to be filtered out by aggressive regularisation using the Least Absolute Shrinkage and Selection Operator (LASSO) resulting in a sparse model with comparable or even better performance than the original pre-trained black-box. Besidesbeing theoretically well-founded, the decomposition of a black-box multivariate probabilistic binary classifier into a General Additive Model (GAM) comprising a linear combination of non-linear functions of one or two variables provides full interpretability. In effect this extends logistic regression into non-linear modelling without the need for manual intervention by way of variable transformations, using the pre-trained model as a seed. The application of the proposed methodology to existing machine learning models is demonstrated using the Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Random Forests (RF) and Gradient Boosting Machines (GBM), to model a data frame from a well-known benchmark dataset available from Physionet, the Medical Information Mart for Intensive Care (MIMIC-III). Both the classification performance and plausibility of clinical interpretation compare favourably with other state-of-the-art sparse models namely Sparse Additive Models (SAM) and the Explainable Boosting Machine (EBM).

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Section: Related Workmentioning

confidence: 99%

Towards interpretable machine learning for clinical decision support

Walters

Ortega‐Martorell

Olier

et al. 2022

2022 International Joint Conference on Neural Networks (IJCNN)

View full text Add to dashboard Cite

show abstract

“…Recently there has been a resurgence of interest in GAMs [11,12] in particular through implementations as GANNs. These models sit firmly at the interface between computational intelligence and traditional statistics, since they permit rigorous computation of relevant statistical measures such as odds ratios for the influence of specific effects [12].…”

Section: Introductionmentioning

confidence: 99%

Explaining the Neural Network: A Case Study to Model the Incidence of Cervical Cancer

Lisboa

Ortega‐Martorell

Olier

2020

Information Processing and Management of Uncertainty in Knowledge-Based Systems

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Neural networks are frequently applied to medical data. We describe how complex and imbalanced data can be modelled with simple but accurate neural networks that are transparent to the user. In the case of a data set on cervical cancer with 753 observations excluding, missing values, and 32 covariates, with a prevalence of 73 cases (9.69%), we explain how model selection can be applied to the Multi-Layer Perceptron (MLP) by deriving a representation using a General Additive Neural Network.The model achieves an AUROC of 0.621 CI [0.519,0.721] for predicting positive diagnosis with Schiller's test. This is comparable with the performance obtained by a deep learning network with an AUROC of 0.667 [1]. Instead of using all covariates, the Partial Response Network (PRN) involves just 2 variables, namely the number of years on Hormonal Contraceptives and the number of years using IUD, in a fully explained model. This is consistent with an additive non-linear statistical approach, the Sparse Additive Model [2] which estimates non-linear components in a logistic regression classifier using the backfitting algorithm applied to an ANOVA functional expansion.This paper shows how the PRN, applied to a challenging classification task, can provide insights into the influential variables, in this case correlated with incidence of cervical cancer, so reducing the number of unnecessary variables to be collected for screening. It does so by exploiting the efficiency of sparse statistical models to select features from an ANOVA decomposition of the MLP, in the process deriving a fully interpretable model.

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Explainable generalized additive neural networks with independent neural network training

Ortega-Fernandez,

Sestelo,

Villanueva

2023

Stat Comput

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Neural Networks are one of the most popular methods nowadays given their high performance on diverse tasks, such as computer vision, anomaly detection, computer-aided disease detection and diagnosis or natural language processing. While neural networks are known for their high performance, they often suffer from the so-called “black-box” problem, which means that it is difficult to understand how the model makes decisions. We introduce a neural network topology based on Generalized Additive Models. By training an independent neural network to estimate the contribution of each feature to the output variable, we obtain a highly accurate and explainable deep learning model, providing a flexible framework for training Generalized Additive Neural Networks which does not impose any restriction on the neural network architecture. The proposed algorithm is evaluated through different simulation studies with synthetic datasets, as well as a real-world use case of Distributed Denial of Service cyberattack detection on an Industrial Control System. The results show that our proposal outperforms other GAM-based neural network implementations while providing higher interpretability, making it a promising approach for high-risk AI applications where transparency and accountability are crucial.

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Odds ratio function estimation using a generalized additive neural network

Cited by 4 publications

References 21 publications

Towards interpretable machine learning for clinical decision support

Towards interpretable machine learning for clinical decision support

Explaining the Neural Network: A Case Study to Model the Incidence of Cervical Cancer

Explainable generalized additive neural networks with independent neural network training

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